Targets’ report corresponding to the functions that execute de Mixomics part of the IODA pipeline…

Raw data (before data prep)

Data loaded from file data/new.raw.data.Rda (Features in rows; Samples in columns)

##             A0FJ     A13E     A0G0     A0SX     A143     A0DA      A0B3     A0I2
## RTN2    4.362183 1.984492 1.727323 4.363996 2.447562 4.770798 3.3520618 1.810382
## NDRG2   7.533461 7.455194 8.079968 5.793750 7.158993 8.748061 5.0984040 3.791965
## CCDC113 3.956124 5.427623 2.227300 3.544866 4.691256 4.305401 0.5932056 2.719169
## FAM63A  4.457170 5.440957 5.543480 4.737114 4.808728 5.307480 5.2175851 4.355919
## ACADS   2.256817 4.028813 2.629855 4.269101 2.442135 3.239909 3.8851534 4.200249
## GMDS    6.017940 4.341692 6.363030 4.001104 7.029723 4.236539 5.9178858 4.830286
## HLA-H   5.006907 6.178668 6.039563 7.087633 5.936138 6.909727 8.0433411 9.130370
## SEMA4A  3.217812 2.864659 5.946028 5.007565 5.901459 6.591109 6.5328925 4.982386
## ETS2    4.734446 5.411029 5.651670 5.902449 6.641225 5.858016 6.3091167 5.304488
## LIMD2   5.099598 4.211397 3.304513 5.479451 5.508654 3.766283 4.1138727 5.149344
##                 A0FJ        A13E        A0G0        A0SX        A143        A0DA         A0B3        A0I2
## YWHAE     0.04913078 -0.07998211 -0.03284989 -0.20532949  0.06019021  0.03076171 -0.107861537  0.64984396
## EIF4EBP1  0.44748623  0.60521842  0.89460973 -0.14132292  0.13176899  0.03299680 -0.037124691 -0.52148657
## TP53BP1   0.91783419  0.05910121  0.51704453 -0.31372867  0.33091238 -0.22027100 -0.544743061 -1.60203535
## ARAF      0.02274147 -0.45985298 -0.19182192 -0.07482347 -0.02435747  0.41861665  0.430503500 -0.18714658
## ACACA    -0.08626782 -0.59269183  0.41117190 -0.85148060  0.76975143 -0.71430870 -0.363474049  1.07761482
## ACCB     -0.41662442 -0.06226840  0.82582859 -0.66341044  0.87347870 -0.21752677 -0.269313837  1.58998239
## PRKAA1    0.28527039 -0.27523360  0.06774184  0.02956373 -0.21653182 -0.06306506 -0.077581092 -0.07753959
## ANLN      0.17231110  0.22210598  0.12199399  1.05494810  0.01378422  0.06025690  0.008872461 -0.05187936
## AR       -1.30760569 -1.62047596 -1.07789444 -1.26705469 -0.60132744 -1.20803848 -1.016297633 -0.42122691
## ARID1A    0.50509449  0.33958160  0.22718066  0.35529767  0.54412514 -0.11094480 -0.233223615 -0.35537533

X heatmap

p <- heatmaply(gene_data, 
        #dendrogram = "row",
        xlab = "", ylab = "", 
        main = "",
        scale = "column",
        margins = c(60,100,40,20),
        grid_color = "white",
        grid_width = 0.0000001,
        titleX = FALSE,
        hide_colorbar = TRUE,
        branches_lwd = 0.1,
        label_names = c("Gene", "Sample", "Value"),
        fontsize_row = 5, fontsize_col = 5,
        labCol = colnames(gene_data),
        labRow = rownames(gene_data),
        heatmap_layers = theme(axis.line=element_blank())
        )
p
# save the widget
# library(htmlwidgets)
# saveWidget(p, file= "~/.../heatmap.html")

Data after scaling

(Features in rows; Samples in columns)

##                 A0FJ       A13E       A0G0        A0SX       A143        A0DA        A0B3        A0I2
## RTN2    -0.339017216 -1.7069589 -1.6932017 -0.77077034 -1.5125563 -0.30827331 -1.10524419 -1.78686096
## NDRG2    1.351995219  1.2645533  1.5640835  0.17573315  1.1455179  1.88852090 -0.09520148 -0.59147057
## CCDC113 -0.555539075  0.1632410 -1.4368410 -1.31303795 -0.2467189 -0.56532934 -2.70090082 -1.23863485
## FAM63A  -0.288367464  0.1704837  0.2635129 -0.52376439 -0.1804445 -0.01184293 -0.02626998 -0.25126554
## ACADS   -1.461656232 -0.5965483 -1.2304330 -0.83359119 -1.5156183 -1.15384143 -0.79691673 -0.34517364
## GMDS     0.543877815 -0.4266024  0.6837324 -1.01100612  1.0725867 -0.60336459  0.37876721  0.03489636
## HLA-H    0.004767261  0.5711851  0.5178763  1.03228877  0.4556130  0.87313916  1.60807992  2.62892171
## SEMA4A  -0.949227217 -1.2288804  0.4699170 -0.34472468  0.4360478  0.69715404  0.73447246  0.12665046
## ETS2    -0.140516320  0.1542277  0.3189866  0.24769245  0.8534059  0.29223902  0.60504583  0.32095851
## LIMD2    0.054192600 -0.4973748 -0.8845059 -0.03233419  0.2144367 -0.86310523 -0.66463095  0.22736789
##                  A0FJ        A13E        A0G0        A0SX        A143        A0DA        A0B3        A0I2
## YWHAE     0.048086807 -0.06835724 -0.03680699 -0.37011394  0.15445759  0.17963331 -0.15661380  0.82210223
## EIF4EBP1  0.718910137  0.91548414  1.15538628 -0.26601103  0.24245583  0.18352769 -0.01907974 -0.45387748
## TP53BP1   1.510967539  0.13134476  0.67004904 -0.54641881  0.48728075 -0.25776165 -1.00604509 -1.63096482
## ARAF      0.003647689 -0.61379274 -0.24115592 -0.15785359  0.05051551  0.85542489  0.89013248 -0.08966685
## ACACA    -0.179921971 -0.80452867  0.53395617 -1.42104063  1.02678515 -1.11856420 -0.65360269  1.28809104
## ACCB     -0.736236460 -0.04292312  1.06697229 -1.11515560  1.15430643 -0.25298015 -0.47052645  1.84623463
## PRKAA1    0.445741613 -0.34870802  0.09249759  0.01192604 -0.18574175  0.01615120 -0.09773935  0.02973266
## ANLN      0.255520229  0.36539426  0.16223545  1.67965330  0.09740652  0.23102520  0.07035279  0.05768544
## AR       -2.236632707 -2.28026681 -1.38014859 -2.09694741 -0.65880568 -1.97883024 -1.92289139 -0.34466042
## ARID1A    0.815921388  0.53407098  0.29744656  0.54171311  0.74940241 -0.06727361 -0.40035602 -0.27292552

X heatmap scaled

p <- heatmaply(gene_sc, 
        #dendrogram = "row",
        xlab = "", ylab = "", 
        main = "",
        scale = "column",
        margins = c(60,100,40,20),
        grid_color = "white",
        grid_width = 0.0000001,
        titleX = FALSE,
        hide_colorbar = TRUE,
        branches_lwd = 0.1,
        label_names = c("Gene", "Sample", "Value"),
        fontsize_row = 5, fontsize_col = 5,
        labCol = colnames(gene_sc),
        labRow = rownames(gene_sc),
        heatmap_layers = theme(axis.line=element_blank())
        )
p
# save the widget
# library(htmlwidgets)
# saveWidget(p, file= "~/.../heatmap.html")

Correlation matrix

plot_corr_matrix(tar_read(X), tar_read(Y), p.resultsDir)
## png 
##   2

Correlation matrix

heatmaply_cor(
  cor(X),
  xlab = "Genes",
  ylab = "Genes"#,
  #k_col = 2,
  #k_row = 2
)
heatmaply_cor(
  cor(Y),
  xlab = "Prots",
  ylab = "Prots"#,
  #k_col = 2,
  #k_row = 2
)

rCCA results

tar_read(rCCA)
## 
## Call:
##  rcc(X = X, Y = Y, ncomp = 3, lambda1 = lambda1, lambda2 = lambda2) 
## 
##  rCCA with 3 components and regularization parameters 0.1 and 0.02 for the X and Y data. 
##  You entered data X of dimensions : 150 200 
##  You entered data Y of dimensions : 150 110 
## 
##  Main numerical outputs: 
##  -------------------- 
##  canonical correlations: see object$cor 
##  loading vectors: see object$loadings 
##  variates: see object$variates 
##  variable names: see object$names

Individual samples plot

plot_indiv(tar_read(rCCA), p.resultsDir)

## png 
##   2

Correlation circles plots

Correlation circles plots (at cutoff points: 0.5, 0.6, 0.7)

for(i in 1:length(p.circ.cutOffs)){
  plot_corrCirc(tar_read(rCCA), p.resultsDir, cutOff = p.circ.cutOffs[i])
}

rCCA details

str(tar_read(rCCA_tagged))
## List of 11
##  $ call         : language rcc(X = X, Y = Y, ncomp = 3, lambda1 = lambda1, lambda2 = lambda2)
##  $ X            : num [1:150, 1:200] -0.339 -1.707 -1.693 -0.771 -1.513 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
##   .. ..$ : chr [1:200] "x.RTN2" "x.NDRG2" "x.CCDC113" "x.FAM63A" ...
##   ..- attr(*, "scaled:center")= Named num [1:150] 5 5.13 5.03 5.53 5.13 ...
##   .. ..- attr(*, "names")= chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
##   ..- attr(*, "scaled:scale")= Named num [1:150] 1.88 1.84 1.95 1.51 1.77 ...
##   .. ..- attr(*, "names")= chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
##  $ Y            : num [1:150, 1:110] 0.0481 -0.0684 -0.0368 -0.3701 0.1545 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
##   .. ..$ : chr [1:110] "y.YWHAE" "y.EIF4EBP1" "y.TP53BP1" "y.ARAF" ...
##   ..- attr(*, "scaled:center")= Named num [1:150] 0.02058 -0.03237 -0.00422 0.02223 -0.06545 ...
##   .. ..- attr(*, "names")= chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
##   ..- attr(*, "scaled:scale")= Named num [1:150] 0.594 0.696 0.778 0.615 0.813 ...
##   .. ..- attr(*, "names")= chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
##  $ ncomp        : num 3
##  $ method       : chr "ridge"
##  $ cor          : Named num [1:110] 0.985 0.951 0.944 0.937 0.928 ...
##   ..- attr(*, "names")= chr [1:110] "1" "2" "3" "4" ...
##  $ loadings     :List of 2
##   ..$ X: num [1:200, 1:3] -0.00629 0.05236 -0.03147 -0.02301 0.00386 ...
##   .. ..- attr(*, "dimnames")=List of 2
##   .. .. ..$ : chr [1:200] "RTN2" "NDRG2" "CCDC113" "FAM63A" ...
##   .. .. ..$ : NULL
##   ..$ Y: num [1:110, 1:3] 0.0237 -0.0155 -0.0122 -0.0812 -0.0339 ...
##   .. ..- attr(*, "dimnames")=List of 2
##   .. .. ..$ : chr [1:110] "YWHAE" "EIF4EBP1" "TP53BP1" "ARAF" ...
##   .. .. ..$ : NULL
##  $ variates     :List of 2
##   ..$ X: num [1:150, 1:3] 1.414 1.612 1.207 0.982 1.215 ...
##   .. ..- attr(*, "dimnames")=List of 2
##   .. .. ..$ : chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
##   .. .. ..$ : NULL
##   ..$ Y: num [1:150, 1:3] 1.41 1.6 1.22 1.07 1.21 ...
##   .. ..- attr(*, "dimnames")=List of 2
##   .. .. ..$ : chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
##   .. .. ..$ : NULL
##  $ names        :List of 4
##   ..$ sample  : chr [1:150] "A0FJ" "A13E" "A0G0" "A0SX" ...
##   ..$ colnames:List of 2
##   .. ..$ X: chr [1:200] "x.RTN2" "x.NDRG2" "x.CCDC113" "x.FAM63A" ...
##   .. ..$ Y: chr [1:110] "y.YWHAE" "y.EIF4EBP1" "y.TP53BP1" "y.ARAF" ...
##   ..$ blocks  : chr [1:2] "X" "Y"
##   ..$ data    : chr [1:2] "X" "Y"
##  $ lambda       : Named num [1:2] 0.1 0.02
##   ..- attr(*, "names")= chr [1:2] "lambda1" "lambda2"
##  $ prop_expl_var:List of 2
##   ..$ X: Named num [1:3] 0.0742 0.0174 0.0184
##   .. ..- attr(*, "names")= chr [1:3] "comp1" "comp2" "comp3"
##   ..$ Y: Named num [1:3] 0.232 0.0444 0.0213
##   .. ..- attr(*, "names")= chr [1:3] "comp1" "comp2" "comp3"
##  - attr(*, "class")= chr "rcc"

Correlation network

str(tar_read(rCCA_network))
## List of 3
##  $ gR    :List of 10
##   ..$ :List of 1
##   .. ..$ x.FAM63A: 'igraph.vs' Named int 42
##   .. .. ..- attr(*, "names")= chr "y.ESR1"
##   .. .. ..- attr(*, "env")=<weakref> 
##   .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
##   ..$ :List of 1
##   .. ..$ x.LIMD2: 'igraph.vs' Named int 42
##   .. .. ..- attr(*, "names")= chr "y.ESR1"
##   .. .. ..- attr(*, "env")=<weakref> 
##   .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
##   ..$ :List of 1
##   .. ..$ x.RTKN2: 'igraph.vs' Named int [1:2] 42 43
##   .. .. ..- attr(*, "names")= chr [1:2] "y.ESR1" "y.GATA3"
##   .. .. ..- attr(*, "env")=<weakref> 
##   .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
##   ..$ :List of 1
##   .. ..$ x.ARL4C: 'igraph.vs' Named int 42
##   .. .. ..- attr(*, "names")= chr "y.ESR1"
##   .. .. ..- attr(*, "env")=<weakref> 
##   .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
##   ..$ :List of 1
##   .. ..$ x.ASPM: 'igraph.vs' Named int [1:7] 38 39 40 41 42 43 45
##   .. .. ..- attr(*, "names")= chr [1:7] "y.ASNS" "y.CDK1" "y.CCNB1" "y.CCNE1" ...
##   .. .. ..- attr(*, "env")=<weakref> 
##   .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
##   ..$ :List of 1
##   .. ..$ x.KDM4B: 'igraph.vs' Named int [1:8] 38 39 40 41 42 43 44 45
##   .. .. ..- attr(*, "names")= chr [1:8] "y.ASNS" "y.CDK1" "y.CCNB1" "y.CCNE1" ...
##   .. .. ..- attr(*, "env")=<weakref> 
##   .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
##   ..$ :List of 1
##   .. ..$ x.SNORA8: 'igraph.vs' Named int 43
##   .. .. ..- attr(*, "names")= chr "y.GATA3"
##   .. .. ..- attr(*, "env")=<weakref> 
##   .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
##   ..$ :List of 1
##   .. ..$ x.ZNF552: 'igraph.vs' Named int [1:9] 37 38 39 40 41 42 43 44 45
##   .. .. ..- attr(*, "names")= chr [1:9] "y.AR" "y.ASNS" "y.CDK1" "y.CCNB1" ...
##   .. .. ..- attr(*, "env")=<weakref> 
##   .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
##   ..$ :List of 1
##   .. ..$ x.ASF1B: 'igraph.vs' Named int 42
##   .. .. ..- attr(*, "names")= chr "y.ESR1"
##   .. .. ..- attr(*, "env")=<weakref> 
##   .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
##   ..$ :List of 1
##   .. ..$ x.FUT8: 'igraph.vs' Named int [1:4] 37 41 42 43
##   .. .. ..- attr(*, "names")= chr [1:4] "y.AR" "y.CCNE1" "y.ESR1" "y.GATA3"
##   .. .. ..- attr(*, "env")=<weakref> 
##   .. .. ..- attr(*, "graph")= chr "76b9ba7e-1237-11ec-8000-010000000000"
##   ..- attr(*, "class")= chr "igraph"
##  $ M     : num [1:200, 1:110] 0 0 0 0 0 0 0 0 0 0 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:200] "x.RTN2" "x.NDRG2" "x.CCDC113" "x.FAM63A" ...
##   .. ..$ : chr [1:110] "y.YWHAE" "y.EIF4EBP1" "y.TP53BP1" "y.ARAF" ...
##  $ cutoff: num 0.5
str(rCCA_network$M)
##  num [1:200, 1:110] 0 0 0 0 0 0 0 0 0 0 ...
##  - attr(*, "dimnames")=List of 2
##   ..$ : chr [1:200] "x.RTN2" "x.NDRG2" "x.CCDC113" "x.FAM63A" ...
##   ..$ : chr [1:110] "y.YWHAE" "y.EIF4EBP1" "y.TP53BP1" "y.ARAF" ...

Correlation network

Correlation Heatmap

mat <- data.matrix(rCCA_network$M)
colnames(mat) <- colnames(rCCA_network$M)
rownames(mat) <- rownames(rCCA_network$M)
str(mat)
##  num [1:200, 1:110] 0 0 0 0 0 0 0 0 0 0 ...
##  - attr(*, "dimnames")=List of 2
##   ..$ : chr [1:200] "x.RTN2" "x.NDRG2" "x.CCDC113" "x.FAM63A" ...
##   ..$ : chr [1:110] "y.YWHAE" "y.EIF4EBP1" "y.TP53BP1" "y.ARAF" ...
which(rowSums(abs(mat))!=0)
##  x.FAM63A   x.LIMD2   x.RTKN2   x.ARL4C    x.ASPM   x.KDM4B  x.SNORA8  x.ZNF552   x.ASF1B    x.FUT8   x.EPHB3 
##         4        10        15        19        24        25        29        34        41        42        49 
##    x.PRNP x.C4orf34   x.LRIG1    x.STC2 x.LAPTM4B   x.CSRP2     x.LYN x.SLC43A3     x.HN1  x.TTC39A    x.RIN3 
##        51        61        62        73        82        94        96       101       102       103       107 
## x.C1orf38    x.NTN4   x.FMNL2    x.E2F1   x.CCNA2  x.NCAPG2    x.LMO4   x.NCOA7   x.RUNX3  x.SLC5A6   x.PREX1 
##       109       112       116       128       136       141       153       158       162       163       165 
##   x.PLAUR   x.MEX3A  x.SEMA3C 
##       166       173       195
which(colSums(abs(mat))!=0)
##     y.AR   y.ASNS   y.CDK1  y.CCNB1  y.CCNE1   y.ESR1  y.GATA3 y.INPP4B    y.PGR 
##        9       11       26       33       35       40       45       51       71
mat <- mat[which(rowSums(abs(mat))!=0), which(colSums(abs(mat))!=0)]

head(mat)
##          y.AR     y.ASNS     y.CDK1    y.CCNB1    y.CCNE1     y.ESR1    y.GATA3  y.INPP4B      y.PGR
## x.FAM63A    0  0.0000000  0.0000000  0.0000000  0.0000000  0.5089002  0.0000000 0.0000000  0.0000000
## x.LIMD2     0  0.0000000  0.0000000  0.0000000  0.0000000 -0.5441847  0.0000000 0.0000000  0.0000000
## x.RTKN2     0  0.0000000  0.0000000  0.0000000  0.0000000 -0.5478270 -0.5000234 0.0000000  0.0000000
## x.ARL4C     0  0.0000000  0.0000000  0.0000000  0.0000000 -0.5029863  0.0000000 0.0000000  0.0000000
## x.ASPM      0  0.5453709  0.5584230  0.5785073  0.5574985 -0.6557532 -0.6001769 0.0000000 -0.5451481
## x.KDM4B     0 -0.5945328 -0.5641563 -0.5905025 -0.5424131  0.6871384  0.6091690 0.5531793  0.6028017
p <- heatmaply_cor(
    mat,
    xlab = "Prots",
    ylab = "Genes"#,
    #k_col = 2,
    #k_row = 2
)

p
# save the widget
# library(htmlwidgets)
# saveWidget(p, file= "~/.../heatmap.html")

CIM heatmap

plot_cim(tar_read(rCCA_tagged), p.resultsDir, x.lab = p.x.lab, y.lab = p.y.lab) 
## png 
##   2

CIM Heatmap